2024-03-20 14:29:57 +01:00
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pipeline {
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agent any
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2024-03-26 18:29:52 +01:00
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parameters {
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string (
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name: 'KAGGLE_USERNAME',
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defaultValue: 'ardenw',
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description: 'Kaggle username'
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)
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password (
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2024-04-02 20:24:31 +02:00
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name: 'API_KEY',
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2024-03-26 18:29:52 +01:00
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defaultValue: '',
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description: 'Kaggle API key'
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)
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string (
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name: 'DATA_TRAIN_RATIO',
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2024-03-26 18:45:26 +01:00
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defaultValue: '80',
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description: 'Train data ratio percentage'
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2024-03-26 18:29:52 +01:00
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)
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string (
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name: 'CUTOFF',
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defaultValue: '500',
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description: 'Cutoff value'
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)
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}
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2024-03-20 14:29:57 +01:00
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stages {
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2024-03-20 14:58:46 +01:00
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stage('Checkout repository') {
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2024-03-20 14:29:57 +01:00
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steps {
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2024-03-26 18:29:52 +01:00
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checkout scm
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2024-03-20 14:58:46 +01:00
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}
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}
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2024-04-02 20:24:31 +02:00
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stage('Download dataset') {
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steps {
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}", "KAGGLE_KEY=${params.KAGGLE_KEY}"]) {
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sh "kaggle datasets download -d nikhil7280/student-performance-multiple-linear-regression --unzip"
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}
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}
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}
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2024-04-02 20:02:32 +02:00
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stage('Prepare dataset') {
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agent {
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dockerfile {
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filename 'Dockerfile'
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reuseNode true
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}
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}
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2024-03-20 14:58:46 +01:00
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steps {
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2024-04-02 20:02:32 +02:00
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sh "chmod +x ./get_dataset.py"
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sh "python ./get_dataset.py"
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2024-04-02 20:02:32 +02:00
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archiveArtifacts artifacts: 'dataset.csv,df_train.csv,df_test.csv', onlyIfSuccessful: true
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}
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2024-03-20 14:29:57 +01:00
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}
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}
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2024-03-26 18:34:28 +01:00
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}
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